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swimlane-mcp

by opositatest
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

list_comments

Read-onlyIdempotent

Find KanbanFlow comments that mention a person or contain text, returning author, board, and task info.

Instructions

Searches the comments of every configured KanbanFlow board and returns the most recent ones, newest first, with author, board and task (name, column, url) resolved. Comments are found through the board activity log (taskCommentCreated), so only tasks commented inside the requested window are looked at. Pass person to find comments that mention someone: KanbanFlow keeps no structured mention field, so a mention is the person's full name (or first name) inside the comment text, matched as a whole word and case-insensitively (person.textSearched says which names were used); use text for a handle or exact phrase. The comment text is returned verbatim. meta reports the window (default: last 30 days), whether the activity log was read completely, how many tasks and API requests it took, and everything that was left out.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toNoEnd of the window, ISO 8601 UTC (default: now).
fromNoStart of the window, ISO 8601 UTC (default: 30 days before "to").
textNoOnly comments whose text contains this, case-insensitive (use it for a handle or an exact phrase).
limitNoMaximum comments returned, newest first (default 20, max 200).
boardsNoBoard ids or exact names to search (see list_boards). Default: every configured board.
personNoOnly comments that mention this person (user id, email, full name or part of the name). Use "me" for the configured user. KanbanFlow has no structured mention field, so a comment counts as a mention when its text contains one of the person's names on that board as a whole word (case-insensitive, so '@Ada' matches 'Ada Lovelace'): the full name and the first name are looked for. `person.textSearched` says which names were used. Omit it to list comments regardless of mentions.
maxTasksNoMaximum tasks whose comments are read (default 25, max 100); the most recently commented tasks are read first. Each one costs one API request.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A4.6/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare read-only/idempotent/open-world, and the description adds real behavioral context beyond them: comments are discovered via the `taskCommentCreated` activity log, only tasks commented inside the window are examined, mention matching is a heuristic whole-word name match, and `meta` reports completeness and omissions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the core purpose, then progressive detail on windowing, mention matching, and meta. The final sentence is dense with meta fields but every sentence carries information; no obvious filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, and the description compensates by describing returned fields (author, board, resolved task name/column/url, verbatim comment text) and the contents of `meta`. Nothing an agent needs to call the tool correctly appears to be missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema coverage the baseline is 3, but the description adds semantics not in the schema: it explains that `person.textSearched` reveals which names were matched and that `text` is for handles/exact phrases. This meaningfully augments the parameter documentation rather than repeating it.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (searches/returns) and resource (comments across configured KanbanFlow boards), plus ordering (newest first) and resolution (author, board, task name/column/url). It is clearly distinguishable from siblings like list_tasks, search_tasks, and get_task.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly routes the agent between `person` (mention search) and `text` (handle or exact phrase), and explains the activity-log mechanism that constrains the window. It stops short of naming sibling alternatives (e.g. when to use search_tasks instead), so it is clear context without explicit exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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